Lecture #23 ggplot III

ggplot (continued)

Installing `colorblindr`

install.packages("remotes")
library(remotes)
remotes::install_github("clauswilke/colorblindr") 
# update all of the recommended packages when prompted
# if an error is generated, re-start RStudio and re-run this line of code

Preliminaries

library(ggplot2)
library(ggthemes)
library(patchwork)
library(colorblindr)
library(cowplot)
library(colorspace)
library(wesanderson)
library(ggsci)
library(TeachingDemos)

char2seed("Dark Star")
d <- mpg

Bar plots

# use to plot the counts of rows for a categorical variable
table(d$drv)

## 
##   4   f   r 
## 103 106  25

p1 <- ggplot(d,aes(x=drv)) + geom_bar(color="black",fill="cornsilk")
print(p1)

# aesthetic mapping gives multiple groups for each bar
p1 <- ggplot(d,aes(x=drv,fill=fl)) + geom_bar()
print(p1)

# stacked, but need to adjust color transparency, which is "alpha"
p1 <- ggplot(d,aes(x=drv,fill=fl)) + geom_bar(alpha = 0.3, position="identity")
print(p1)

# better to use position = fill for stacking, but with equivalent height
p1 <- ggplot(d,aes(x=drv,fill=fl)) + geom_bar(position="fill")
print(p1)

# best to use position = dodge for multiple bars
p1 <- ggplot(d,aes(x=drv,fill=fl)) + geom_bar(position="dodge",color="black",size=1)
print(p1)

# more typical "bar plot" has heights as the values themselves
dTiny <- tapply(X=d$hwy,INDEX=as.factor(d$fl),FUN=mean) #calculate the means
dTiny <- data.frame(hwy=dTiny) # create a single-column data frame
dTiny <- cbind(fl=row.names(dTiny),dTiny) # 

p2 <- ggplot(dTiny, aes(x=fl,y=hwy,fill=fl)) +
  geom_col()
print(p2)

#### Use a box plot instead of standard "means" bars!

# basic boxplot is simple and informative
p1 <- ggplot(d,aes(x=fl,y=hwy,fill=fl)) +
               geom_boxplot()
print(p1)

# now overlay the raw data
p1 <- ggplot(d,aes(x=fl,y=hwy)) +
               geom_boxplot(fill="thistle",outlier.shape=NA) + 
  # geom_point()
  geom_point(position=position_jitter(width=0.1,height=0.7),color="grey60",size=2)

print(p1)

### Using Colors

Aesthetics

Colors that are attractive - large geoms (fills) - pale colors - small geoms(lines,points) - bright colors
Colors that highlight elements - pale, grey to de-emphasize - bright or saturated colors to emphasize
Colors that are visible to the color blind
Colors that convert well to black and white

Information content

Discrete scale

colors to group similar treatments
neutral colors (black,grey,white) to indicate control groups
Symbolic colors (heat=red, cool = blue, photosynthesis/growth=green, oligotrophic=blue, eutrophic=brown, infected=red)
Colors that map to chemical stains or gels, or even colors of organisms

Continuous scale

monochromatic (differing shades of 1 color)
2 tone chromatic scale (from color x to color y)
3 tone divergent scale (from color x through color y to color z)

Use color information within and between graphs

show color names, hex in base R
show color schemes in colorbrewer

Color visualizations

my_cols <- c("thistle","tomato","cornsilk","cyan","chocolate")
demoplot(my_cols,"map")

demoplot(my_cols,"bar")

demoplot(my_cols,"scatter")

demoplot(my_cols,"heatmap")

demoplot(my_cols,"spine")

demoplot(my_cols,"perspective")

Working with black and white

# gray function versus gray colors

# built in greys (0 = black, 100 = white
my_greys <- c("grey20","grey50","grey80")
demoplot(my_greys,"bar")

my_greys2 <- grey(seq(from=0.1,to=0.9,length.out=10))         
demoplot(my_greys2,"heatmap")

# converting color plots to black and white
p1 <- ggplot(d,aes(x=as.factor(cyl),y=cty,fill=as.factor(cyl))) + geom_boxplot() 
plot(p1)

# default colors look identical in black white
p1_des<- colorblindr::edit_colors(p1, desaturate)
plot(p1_des)

# custom colors not pretty, but convert ok to bw
p2 <- p1 + scale_fill_manual(values=c("red","blue","green","yellow"))
plot(p2)

p2_des<- colorblindr::edit_colors(p2, desaturate)
plot(p2_des)

using alpha transparency for histograms

x1 <- rnorm(n=100,mean=0)
x2 <- rnorm(n=100,mean=2.7)
dFrame <- data.frame(v1=c(x1,x2))
lab <- rep(c("Control","Treatment"),each=100)
dFrame <- cbind(dFrame,lab)
str(dFrame)

## 'data.frame':    200 obs. of  2 variables:
##  $ v1 : num  -0.0306 1.4022 0.166 2.5987 -1.1781 ...
##  $ lab: chr  "Control" "Control" "Control" "Control" ...

h1 <- ggplot(dFrame,aes(x=v1,fill=lab))
h1 + geom_histogram(position="identity",alpha=0.5,color="black")

Color customizations

d <- mpg
# --------- discrete classification
# scale_fill_manual for boxplots,bars
# scale_color_manual for points, lines

# boxplot no color
p_fil <- ggplot(d,aes(x=as.factor(cyl),y=cty))
p_fil + geom_boxplot()

# boxplot default ggplot fill
p_fil <- ggplot(d,aes(x=as.factor(cyl),y=cty,fill=as.factor(cyl))) + geom_boxplot()
plot(p_fil)

# create custom color palette
my_cols <- c("red","brown","blue","orange")


# boxplot with custom colors for fill
p_fil + scale_fill_manual(values=my_cols)

# scatterplot with no color
p_col <- ggplot(d,aes(x=displ,y=cty))
p_col + geom_point(size=3)

# scatterplot default ggplot colors
p_col <- ggplot(d,aes(x=displ,y=cty,col=as.factor(cyl))) + geom_point(size=3)
plot(p_col)

# scatterplot with custom colors for point color
p_col + scale_color_manual(values=my_cols)

# ------- continuous classification (color gradient)

# default color gradient
p_grad <- ggplot(d,aes(x=displ,y=cty,col=hwy)) + geom_point(size=3)
plot(p_grad)

# custom sequential gradient (2-colors)
p_grad + scale_color_gradient(low="green", high="red")

# custom diverging gradient (3-colors)
mid <- median(d$cty)
p_grad + scale_color_gradient2(midpoint=mid,
                               low="blue",
                               mid="white",
                               high="red")

# custom diverging gradient (n-colors
p_grad + scale_color_gradientn(colors=c("blue","green","yellow","purple","orange"))

Tour of color palettes

Wes Anderson palettes

library(wesanderson)
print(wes_palettes)

## $BottleRocket1
## [1] "#A42820" "#5F5647" "#9B110E" "#3F5151"
## [5] "#4E2A1E" "#550307" "#0C1707"
## 
## $BottleRocket2
## [1] "#FAD510" "#CB2314" "#273046" "#354823"
## [5] "#1E1E1E"
## 
## $Rushmore1
## [1] "#E1BD6D" "#EABE94" "#0B775E" "#35274A"
## [5] "#F2300F"
## 
## $Rushmore
## [1] "#E1BD6D" "#EABE94" "#0B775E" "#35274A"
## [5] "#F2300F"
## 
## $Royal1
## [1] "#899DA4" "#C93312" "#FAEFD1" "#DC863B"
## 
## $Royal2
## [1] "#9A8822" "#F5CDB4" "#F8AFA8" "#FDDDA0"
## [5] "#74A089"
## 
## $Zissou1
## [1] "#3B9AB2" "#78B7C5" "#EBCC2A" "#E1AF00"
## [5] "#F21A00"
## 
## $Darjeeling1
## [1] "#FF0000" "#00A08A" "#F2AD00" "#F98400"
## [5] "#5BBCD6"
## 
## $Darjeeling2
## [1] "#ECCBAE" "#046C9A" "#D69C4E" "#ABDDDE"
## [5] "#000000"
## 
## $Chevalier1
## [1] "#446455" "#FDD262" "#D3DDDC" "#C7B19C"
## 
## $FantasticFox1
## [1] "#DD8D29" "#E2D200" "#46ACC8" "#E58601"
## [5] "#B40F20"
## 
## $Moonrise1
## [1] "#F3DF6C" "#CEAB07" "#D5D5D3" "#24281A"
## 
## $Moonrise2
## [1] "#798E87" "#C27D38" "#CCC591" "#29211F"
## 
## $Moonrise3
## [1] "#85D4E3" "#F4B5BD" "#9C964A" "#CDC08C"
## [5] "#FAD77B"
## 
## $Cavalcanti1
## [1] "#D8B70A" "#02401B" "#A2A475" "#81A88D"
## [5] "#972D15"
## 
## $GrandBudapest1
## [1] "#F1BB7B" "#FD6467" "#5B1A18" "#D67236"
## 
## $GrandBudapest2
## [1] "#E6A0C4" "#C6CDF7" "#D8A499" "#7294D4"
## 
## $IsleofDogs1
## [1] "#9986A5" "#79402E" "#CCBA72" "#0F0D0E"
## [5] "#D9D0D3" "#8D8680"
## 
## $IsleofDogs2
## [1] "#EAD3BF" "#AA9486" "#B6854D" "#39312F"
## [5] "#1C1718"

demoplot(wes_palettes$BottleRocket1,"pie")

demoplot(wes_palettes[[2]][1:3],"bar")

my_cols <- wes_palettes$GrandBudapest2[1:4]
p_fil + scale_fill_manual(values=my_cols)

Color Brewer palettes

library(RColorBrewer)
display.brewer.all()

display.brewer.all(colorblindFriendly=TRUE)

demoplot(brewer.pal(4,"Accent"),"bar")

demoplot(brewer.pal(11,"Spectral"),"heatmap")

my_cols <- c("grey75",brewer.pal(3,"Blues"))
p_fil + scale_fill_manual(values=my_cols)

# nice for seeing hex values!
library(scales)
show_col(my_cols)

Viridis palettes

#### Making a heat map
xVar <- 1:30
yVar <- 1:5
myData <- expand.grid(xVar=xVar,yVar=yVar)
head(myData)

##   xVar yVar
## 1    1    1
## 2    2    1
## 3    3    1
## 4    4    1
## 5    5    1
## 6    6    1

zVar <- myData$xVar + myData$yVar + 2*rnorm(n=150)
myData <- cbind(myData,zVar)
head(myData)

##   xVar yVar     zVar
## 1    1    1 1.930717
## 2    2    1 4.783829
## 3    3    1 4.301021
## 4    4    1 3.626993
## 5    5    1 6.506057
## 6    6    1 5.774694

# default gradient colors in ggplot
p4 <- ggplot(myData,aes(x=xVar,y=yVar,fill=zVar)) +
  geom_tile()
print(p4)

# user defined divergent palette
p4 + scale_fill_gradient2(midpoint=19,low="brown",mid=grey(0.8),high="darkblue")

# viridis scale
p4  + scale_fill_viridis_c()

# options viridis, cividis, magma, inferno, plasma
p4 + scale_fill_viridis_c(option="inferno")

#desaturated viridis
p4 <- p4 + geom_tile() + scale_fill_viridis_c() 
p4des<-edit_colors(p4, desaturate)
ggdraw(p4des)

Lecture #23 ggplot III

Nicholas J. Gotelli

27 April 2021

ggplot (continued)

Installing `colorblindr`

Preliminaries

Bar plots

Color visualizations

Working with black and white

using alpha transparency for histograms

Color customizations

Tour of color palettes

Wes Anderson palettes

Color Brewer palettes

Viridis palettes

Lecture #23 ggplot III

Nicholas J. Gotelli

27 April 2021

ggplot (continued)

Installing colorblindr

Preliminaries

Bar plots

Color visualizations

Working with black and white

using alpha transparency for histograms

Color customizations

Tour of color palettes

Wes Anderson palettes

Color Brewer palettes

Viridis palettes

Installing `colorblindr`